The Power of Brand Selfies in Consumer-Generated Brand Images

Hartmann, Jochen; Heitmann, Mark; Schamp, Christina; Netzer, Oded

doi:10.2139/ssrn.3354415

Cited by 13 publications

(16 citation statements)

References 84 publications

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“…One of the major limitations of the lexical approaches for sentiment analysis commonly used in marketing is that they apply a “bag of words” approach—meaning that word order does not matter—and rely solely on the cooccurrence of a word of interest (e.g., “brand”) with positive or negative words (e.g., “great,” “bad”) in the same textual unit (e.g., a review). While dictionary approaches may be an easy way to measure constructs and comparability across data sets, machine learning approaches trained by human-coded data (e.g., Borah and Tellis 2016; Hartmann et al 2018; Hennig-Thurau, Wiertz, and Feldhaus 2015) tend to be the most accurate way of measuring such constructs (Hartmann et al 2019), particularly if the construct is complex or the domain is uncommon. For this reason, researchers should carefully weigh the trade-off between empirical fit and theoretical commensurability, taking care to validate any dictionaries used in the analysis (discussed in the next section).…”

Section: Text Analysis Tools Methods and Metricsmentioning

confidence: 99%

“…Similarly, recent research has developed approaches to analyze images (e.g., Liu, Xuan et al 2018), either characterizing the content of the image or identifying features within an image. Research into the impact of the combination of text and images is sparse (e.g., Hartmann et al 2019). For example, images can be described in terms of their colors.…”

Section: Future Research Agendamentioning

confidence: 99%

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Uniting the Tribes: Using Text for Marketing Insight

Berger¹,

Humphreys²,

Ludwig³

et al. 2019

Journal of Marketing

Self Cite

508

308

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Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.

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Section: Text Analysis Tools Methods and Metricsmentioning

confidence: 99%

Section: Future Research Agendamentioning

confidence: 99%

Uniting the Tribes: Using Text for Marketing Insight

Berger¹,

Humphreys²,

Ludwig³

et al. 2019

Journal of Marketing

Self Cite

508

308

View full text Add to dashboard Cite

show abstract

“…In sum, as illustrated by the case of Cambridge Analytica, the idea that marketers are able to use people's digital traces to gain deep insights into their psychology and thereby to accurately predict and manipulate their behavior as if with a digital “voodoo doll” (Johnson, 2019) is fanciful. To be sure, marketers are able to infer some information about consumers’ psychological traits and preferences from online behavior, such as from the text they write (Berger et al., 2020), the images they post (Hartmann et al., 2020; Liu et al., 2020), and the brands they follow or like (Culotta & Cutler, 2016; Hu, Xu, et al., 2019; Kosinski et al., 2013; Schoenmueller et al., 2020), but these are very crude measures. Moreover, for the most part—as in the case of attempting to use personality measures to predict political preferences—these measures are not very relevant for predicting consumer preferences, and therefore don't much increase the absolute accuracy of prediction.…”

Section: Predicting Future Choices From Past Choices (And Other Behavmentioning

confidence: 99%

Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not

Gal

Simonson

2020

Consumer Psychology Review

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Recent technology advances (e.g., tracking and “AI”) have led to claims and concerns regarding the ability of marketers to anticipate and predict consumer preferences with great accuracy. Here, we consider the capabilities of both traditional techniques (e.g., conjoint analysis) and more recent tools (e.g., advanced machine learning methods) for predicting consumer choices. Our main conclusion is that for most of the more interesting consumer decisions, those that are “new” and non‐habitual, prediction remains hard. In fact, in many cases, prediction has become harder due to the increasing influence of just‐in‐time information (user reviews, online recommendations, new options, etc.) at the point of decision that can neither be measured nor anticipated ex ante. Sophisticated methods and “big data” can in certain contexts improve predictions, but usually only slightly, and prediction remains very imprecise—so much so that it is often a waste of effort. We suggest marketers focus less on trying to predict consumer choices with great accuracy and more on how the information environment affects the choice of their products. We also discuss implications for consumers and policymakers.

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“…Empirical research suggests that DL algorithms excel at extracting patterns and making accurate predictions from unstructured data (such as images, text, and video), making them particularly pertinent to information processing in organizations (Dzyabura, El Kihal, & Ibragimov, 2018;Hartmann, Heitmann, Schamp, & Netzer, 2019;Heitmann, Siebert, Hartmann, & Schamp, 2020). Some studies have found that while 80% of existing marketing data within organizations tends to be unstructured, only a fraction of organizations possess the requisite technical competencies to utilize this type of data (Balducci & Marinova, 2018).…”

Section: Introductionmentioning

confidence: 99%

Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

Shrestha

Krishna

Krogh

2021

Journal of Business Research

140

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The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organizations. In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their analytical capabilities and perhaps help their transition to more creative work. We conceptualize the decision-making process in organizations augmented with DL algorithm outcomes (such as predictions or robust patterns from unstructured data) as deep learning-augmented decision-making (DLADM). We contribute to the understanding and application of DL for decisionmaking in organizations by (a) providing an accessible tutorial on DL algorithms and (b) illustrating DLADM with two case studies drawing on image recognition and sentiment analysis tasks performed on datasets from Zalando, a European e-commerce firm, and Rotten Tomatoes, a review aggregation website for movies, respectively. Finally, promises and challenges of DLADM as well as recommendations for managers in attending to these challenges are also discussed.

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The Power of Brand Selfies in Consumer-Generated Brand Images

Cited by 13 publications

References 84 publications

Uniting the Tribes: Using Text for Marketing Insight

Uniting the Tribes: Using Text for Marketing Insight

Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do not

Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

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